Data Visualization - Lending Club Dataset

Importing the libraries

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
import plotly.io as pio
pio.renderers.default='notebook'

Loading the dataset

# load the dataset into a pandas dataframe
df = pd.read_csv("../data/lending_club_modified_data.csv", index_col=False, dtype='unicode')

# have a quick glance at the dataframe
df.head()

	id	term	grade	emp_title	emp_length_years	purpose	loan_status	loan_type
0	1077501	36 months	B	employer not mentioned	10	credit_card	Fully Paid	Good Loans
1	1077430	60 months	C	ryder	0	car	Charged Off	Bad Loans
2	1077175	36 months	C	employer not mentioned	10	small_business	Fully Paid	Good Loans
3	1076863	36 months	C	air resources board	10	other	Fully Paid	Good Loans
4	1075358	60 months	B	university medical group	1	other	Current	Good Loans

Helper Functions for Pie chart

# function to create dataframe for pie chart
def build_df(var_name):

    # group the data based on var_name and store it as a dictionary
    data = dict(df[var_name].value_counts())

    # convert dictionary to pandas dataframe
    data = pd.DataFrame(data.items(),columns = [var_name,'count'])
    
    return data


# function to plot pie chart
def plot_pretty_pie_chart(label, value, title_name):

    # create a plotly pie chart
    fig = go.Figure(data=[go.Pie(labels=label, values=value)])

    # write label and percentage inside/outside the chart
    fig.update_traces(textinfo='label+percent')

    # pull each pie slices a bit to make it look nicer
    fig.update_traces(pull=[0.1, 0.1, 0.1, 0.1])

    # assign bright color combinations
    fig.update_traces(marker=dict(colors=['#286086', 'maroon', 'blue']))

    # adjust the size of the figure 
    fig.update_layout(width=600, height=550)

    # give title to the chart and align it to the center
    fig.update_layout(title=title_name,title_x=0.5)

    # play around with the font and its size of the body
    fig.update_layout(font_family="Arial", font_size = 15)

    # play around with the font and its size of the title
    fig.update_layout(title = {'font_color':'navy', 'font_size':25})

    # display the chart
    fig.show()

df = df.copy(deep=True).astype({'emp_length_years': 'int64'})

Exploring, analysing and visualizing loan_status variable

Distribution of loan status

loan_status_df = build_df('loan_status')
loan_status_df.style.hide_index()

loan_status	count
Fully Paid	32950
Charged Off	5627
Current	1140

Pie chart showing distribution of loan status

loan_status_type = loan_status_df['loan_status']
loan_status_count = loan_status_df['count']
plot_pretty_pie_chart(loan_status_type, loan_status_count, 'Loan Status')

Out of 39717 data points, the loan status consist of:

• 32,950 fully paid (83%)
• 5,627 charged off (14.2%)
• 1,140 current (2.87%)

Exploring, analysing and Visualizing loan_type variable

loan_type_df = build_df('loan_type')
loan_type_df.style.hide_index()

loan_type	count
Good Loans	34090
Bad Loans	5627

loan_type = loan_type_df['loan_type']
loan_type_count = loan_type_df['count']
plot_pretty_pie_chart(loan_type, loan_type_count, 'Good Loans and Bad Loans')

Helper Function for Loan Segmentation

def generate_df_by_segment_column(segment_col):
    loan_segment_by_col_df = pd.DataFrame(df.groupby([segment_col,'loan_type'])['loan_type'].count())
    loan_segment_by_col_df['percent'] = loan_segment_by_col_df.groupby(level=0).apply(lambda x: round(100 * x / float(x.sum())))
    loan_segment_by_col_df = loan_segment_by_col_df.rename(columns={'loan_type':'count'})
    loan_segment_by_col_df['percent'] = loan_segment_by_col_df['percent'].apply(np.int64)
    return loan_segment_by_col_df

def plot_grouped_bar_chart(data, x_value, y_value, legend, title_name):
    df = data.reset_index()
    fig = px.bar(data_frame=df, x=x_value, y=y_value, color=legend, labels={
                     x_value: x_value.capitalize(),
                     y_value: y_value.capitalize()
                 }, barmode="group", hover_data = {'percent':True,'count':True},color_discrete_sequence=['maroon','#286086'])
    fig.update_layout(width=600, height=550)
    fig.update_layout()
    fig.update_layout(title=title_name,title_x=0.5)
    fig.update_layout(legend_title_text='Loan type')
    fig.update_layout(font_family="Arial", font_size = 15)
    fig.update_layout(title = {'font_color':'navy', 'font_size':25})
    fig.show()

Loan Segment by Term

Generate the segmented df

loan_segment_by_term_df = generate_df_by_segment_column('term')
loan_segment_by_term_df

		count	percent
term	loan_type
36 months	Bad Loans	3227	11
	Good Loans	25869	89
60 months	Bad Loans	2400	23
	Good Loans	8221	77

Plot grouped bar chart based on total count

plot_grouped_bar_chart(loan_segment_by_term_df, 'term', 'count', 'loan_type','Loan Segment by Term (Total)')

Plot grouped bar chart based on proportion

plot_grouped_bar_chart(loan_segment_by_term_df, 'term', 'percent', 'loan_type','Loan Segment by Term (%)')

Loan Segmentation by Grade

Generate the segmented df

loan_segment_by_grade_df = generate_df_by_segment_column('grade')
loan_segment_by_grade_df

		count	percent
grade	loan_type
A	Bad Loans	602	6
	Good Loans	9483	94
B	Bad Loans	1425	12
	Good Loans	10595	88
C	Bad Loans	1347	17
	Good Loans	6751	83
D	Bad Loans	1118	21
	Good Loans	4189	79
E	Bad Loans	715	25
	Good Loans	2127	75
F	Bad Loans	319	30
	Good Loans	730	70
G	Bad Loans	101	32
	Good Loans	215	68

Generate grouped bar chart (total)

plot_grouped_bar_chart(loan_segment_by_grade_df, 'grade', 'count', 'loan_type', 'Loan Segment by Grade (Total)')

Generate grouped bar chart (%)

plot_grouped_bar_chart(loan_segment_by_grade_df, 'grade', 'percent', 'loan_type', 'Loan Segment by Grade (%)')

Helper Function to get Good Loans and Bad Loans Dataframe

# helper functions to get good loan df
def get_segmented_good_loan_df(var_name):
    data = generate_df_by_segment_column(var_name).reset_index()
    data = data[data['loan_type']=='Good Loans']
    return data.reset_index(drop=True)

# helper functions to get bad loan df
def get_segmented_bad_loan_df(var_name):
    data = generate_df_by_segment_column(var_name).reset_index()
    data = data[data['loan_type']=='Bad Loans']
    return data

# helper function to plot line chart
def plot_line_chart(data, x_value, y_value, title_name,color):
    df = data.reset_index()
    fig = px.line(data_frame=df, x=x_value, y=y_value, labels={
                        x_value: 'Employment Length (Years)',
                        y_value: 'Percent'
                    }, hover_data = {'percent':True,'count':True},color_discrete_sequence=[color], orientation='h')

    fig.update_layout(width=600, height=550)
    fig.update_layout()
    fig.update_layout(title=title_name,title_x=0.5)
    #fig.update_layout(legend_title_text='Loan type')
    fig.update_layout(font_family="Times New Roman", font_size = 15)
    fig.update_layout(title = {'font_color':'navy', 'font_size':25})
    fig.show()

Employment Duration and Good Loan

print('------------------------\n\
-1 denotes missing values\n \
0 denotes less than 1 year\n \
10 denotes 10 or more years\n \
------------------------')

emp_length_good_loan_df = get_segmented_good_loan_df('emp_length_years')
#emp_length_good_loan_df.replace({'emp_length_years': {-1: 'Not mentioned', 0:'< 1', 10: '10+'}}, inplace=True)
emp_length_good_loan_df.style.hide_index()

------------------------
-1 denotes missing values
 0 denotes less than 1 year
 10 denotes 10 or more years
 ------------------------

emp_length_years	loan_type	count	percent
-1	Good Loans	847	79
0	Good Loans	3944	86
1	Good Loans	2784	86
2	Good Loans	3821	87
3	Good Loans	3540	86
4	Good Loans	2974	87
5	Good Loans	2824	86
6	Good Loans	1922	86
7	Good Loans	1510	85
8	Good Loans	1276	86
9	Good Loans	1100	87
10	Good Loans	7548	85

print('------------------------\n\
-1 denotes missing values\n \
0 denotes less than 1 year\n \
10 denotes 10 or more years\n \
------------------------')
plot_line_chart(emp_length_good_loan_df, 'emp_length_years', 'percent','Good Loan and Employment Duration','#286086')

------------------------
-1 denotes missing values
 0 denotes less than 1 year
 10 denotes 10 or more years
 ------------------------

print('------------------------\n\
-1 denotes missing values\n \
0 denotes less than 1 year\n \
10 denotes 10 or more years\n \
------------------------')

emp_length_bad_loan_df = get_segmented_bad_loan_df('emp_length_years')
#emp_length_bad_loan_df.replace({'emp_length_years': {-1: 'Not mentioned', 0:'< 1', 10: '10+'}}, inplace=True)
emp_length_bad_loan_df.style.hide_index()

------------------------
-1 denotes missing values
 0 denotes less than 1 year
 10 denotes 10 or more years
 ------------------------

emp_length_years	loan_type	count	percent
-1	Bad Loans	228	21
0	Bad Loans	639	14
1	Bad Loans	456	14
2	Bad Loans	567	13
3	Bad Loans	555	14
4	Bad Loans	462	13
5	Bad Loans	458	14
6	Bad Loans	307	14
7	Bad Loans	263	15
8	Bad Loans	203	14
9	Bad Loans	158	13
10	Bad Loans	1331	15

print('------------------------\n\
-1 denotes missing values\n \
0 denotes less than 1 year\n \
10 denotes 10 or more years\n \
------------------------')

plot_line_chart(emp_length_bad_loan_df, 'emp_length_years', 'percent','Bad Loan and Employment Duration','maroon')

------------------------
-1 denotes missing values
 0 denotes less than 1 year
 10 denotes 10 or more years
 ------------------------

Explore emp_title column

get_segmented_good_loan_df = dict(df['emp_title'].value_counts())

# convert dictionary to pandas dataframe
data = pd.DataFrame(get_segmented_good_loan_df.items(),columns = ['emp_title','count'])
print('The top employers for LC borrowers:')
emp_title_df = build_df('emp_title').head(15)
emp_title_df.style.hide_index()

The top employers for LC borrowers:

emp_title	count
employer not mentioned	2459
us army	212
bank of america	138
at&t	83
walmart	82
wells fargo	71
ibm	69
kaiser permanente	69
verizon wireless	65
ups	63
self	60
usaf	58
usps	58
us air force	57
self employed	57

Purpose for Bad Loans

Generate df

bad_loan_purpose_df = get_segmented_bad_loan_df('purpose').reset_index(drop=True).sort_values(by=['percent'], ascending=False)
bad_loan_purpose_df.style.hide_index()

purpose	loan_type	count	percent
small_business	Bad Loans	475	26
renewable_energy	Bad Loans	19	18
educational	Bad Loans	56	17
moving	Bad Loans	92	16
other	Bad Loans	633	16
debt_consolidation	Bad Loans	2767	15
house	Bad Loans	59	15
medical	Bad Loans	106	15
vacation	Bad Loans	53	14
home_improvement	Bad Loans	347	12
credit_card	Bad Loans	542	11
car	Bad Loans	160	10
major_purchase	Bad Loans	222	10
wedding	Bad Loans	96	10

Generate horizontal bar chart

fig = px.bar(bad_loan_purpose_df, x="percent", y="purpose", labels={
                        'percent': 'Percent',
                        'purpose': 'Purpose'}, orientation='h', hover_data = {'count':True})
fig.update_layout(yaxis={'categoryorder':'total ascending'})
fig.update_layout(width=700, height=550)
fig.update_traces(marker_color='maroon')
fig.update_layout(font_family="Arial", font_size = 15)
fig.update_layout(title='Purpose for Bad Loans',title_x=0.5)
fig.update_layout(title = {'font_color':'Navy', 'font_size':25})
fig.show()